HyperResNet {kerastuneR}

HyperResNet

Description

A ResNet HyperModel.

Usage

HyperResNet(
  include_top = TRUE,
  input_shape = NULL,
  input_tensor = NULL,
  classes = NULL,
  ...
)

Arguments

include_top

whether to include the fully-connected layer at the top of the network.

input_shape

Optional shape list, e.g. '(256, 256, 3)'. One of 'input_shape' or 'input_tensor' must be specified.

input_tensor

Optional Keras tensor (i.e. output of 'layers.Input()') to use as image input for the model. One of 'input_shape' or 'input_tensor' must be specified.

classes

optional number of classes to classify images into, only to be specified if 'include_top' is TRUE, and if no 'weights' argument is specified. **kwargs: Additional keyword arguments that apply to all HyperModels. See 'kerastuner.HyperModel'.

...

Additional keyword arguments that apply to all HyperModels.

Value

a pre-trained ResNet model

Examples

## Not run: 


cifar <- dataset_cifar10()

hypermodel = HyperResNet(input_shape = list(32L, 32L, 3L), classes = 10L)
hypermodel2 = HyperXception(input_shape = list(32L, 32L, 3L), classes = 10L)


tuner = Hyperband(
  hypermodel = hypermodel,
  objective = 'accuracy',
  loss = 'sparse_categorical_crossentropy',
  max_epochs = 1,
  directory = 'my_dir',
  project_name='helloworld')


train_data = cifar$train$x[1:30,1:32,1:32,1:3]
test_data = cifar$train$y[1:30,1] %>% as.matrix()


tuner %>% fit_tuner(train_data,test_data, epochs = 1)

## End(Not run)